Bayesian Methods(here is information for the course on Bootstrap)
- 19. 06. 2017, 14:15-15:45: Kurt-Schumacher 25 - Raum 114
- 19. 06. 2017, 16:15-17:45: Kurt-Schumacher 25, R. 2210/2212
- 20. 06. 2017, 14:15-15:45: Kurt-Schumacher 25 - Raum 114
- 20. 06. 2017, 16:15-17:45: Kurt-Schumacher 25, R. 2210/2212
- 26. 06. 2017, 14:15-15:45: Universitätsplatz 9, R. 0106
- 26. 06. 2017, 16:15-17:45: Universitätsplatz 9, R. 0106
- 27. 06. 2017, 14:15-15:45: Nora-Platiel 4 - Raum 1208
- 27. 06. 2017, 16:15-17:45: Nora-Platiel 4 - Raum 1208
- 28. 06. 2017, 14:15-15:45: Nora-Platiel 4 - Raum 1208
- 28. 06. 2017, 16:15-17:45: Nora-Platiel 4 - Raum 1208
- 06. 07. 2017, 8:00-9:00: Exam
- Introduction and Motivation
- An example: Linear Regression
- Finding posteriors
- Conjugate Priors
- Checking convergence
- Robust regression
- Discrete Choice
- Instrumental Variables
- Errors in Variables
- Interval regression
- Hierarchical Models
- Nonparametric Methods?
- Model Comparison
- If you want to prepare for the lecture (or revise), you can have a look at the Handout
(this is a preliminary version only, please expect changes during the next few weeks).
- Here you will find the exam questions on Saturday, 11:00.
- ... can also be found in the appendix of the handout.
- John K. Kruschke , Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press, 2nd Edition, 2014.
- Hoff, A First Course in Bayesian Statistical Methods. Springer, 2009.
- For our practical examples (during the entire course) we will use the software environment R. I think that it is helpful to coordinate on one environment and R has the advantage of being free and rather powerful.
For the Bayesian parts we will use JAGS. It helps if you have installed R and JAGS on your computer when we start the course.
- Documentation for R is
provided via the built in help system but also through the
- The R Guide, Jason Owen (Easy to read, explains R with the help of examples from basic statistics)
- Simple R, John Verzani (Explains R with the help of examples from basic statistics)
- Einführung in R, Günther Sawitzki (In German. Rather compact introduction.)
- Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic.)
- An Introduction to R, W. N. Venables und D. M. Smith (The focus is more on R as a programming language)
- The R language definition (Concentrates only on R as a programming language.)
- On the JAGS Homepage you go to the files pages, then to Manuals, to find the JAGS user manual.
- We will use the following packages:
runjags, coda, boot, Ecdat, boot, bootstrap, survival, lattice, latticeExtra, lme4, lmtest, quantreg, xtable, plyr, reshape2, sampleSelection. If, e.g., the command
library(Ecdat)generates an error message (
Error in library(Ecdat): There is no package called 'Ecdat'), you have to install the package.
- Installing packages with Microsoft Windows:
Rgui.exeand install packages from the menu
Packages / Install Packages).
- Installing packages from advanced operating systems:
- From within R use the command
install.packages("Ecdat"), e.g., to install the package
- In the lecture we will use RStudio as a front end.
- Documentation for R is provided via the built in help system but also through the R Homepage. Useful are